Antibiotic resistance is emerging as a worldwide medical concern. The pathogen Methicillin-resistant Staphylococcus aureus (MRSA), a cause of severe infections in hospital and recently in community settings, is acquiring resistance to multiple antibiotics. Multi-drug treatments are becoming increasingly important in combating the spread of MRSA and of other drug-resistant bacterial pathogens. Although the effects of multi- drug combinations on bacterial growth have been studied extensively, little is known about their impact on the evolution of drug resistance. Here, we propose a combined theoretical-experimental approach to understand, predict, and control the dynamic processes governing the evolution of antibiotic resistance in multi-drug antibiotic environments. Resistant mutants may arise either by spontaneous mutations or by horizontal gene transfer from other species. After they appear, these resistant bacteria must first out-compete their sensitive cousins before they become established in the population. We will develop a population genetic model to simulate this dynamic population process of resistance evolution in the multi-dimensional environment presented by drug combinations. Novel automated experimental techniques allow us to explore a wide range of drug combinations with respect to their impact on the rate of bacterial proliferation, the differential advantage of resistant bacteria competing with sensitive ones, and the rate of evolutionary adaptation. We will specifically address the following questions: (1) How do drugs combine to affect the "fitness landscape" for evolution of resistance? (2) Could drug combinations be designed to generate conditions which favor sensitive strains over horizontally acquired resistance? and (3) How do such antibiotic combinations affect the rate of spontaneous evolution of resistance? We anticipate that our findings will suggest a new strategy for designing drug combinations with the potential of providing effective antimicrobial treatments that are inherently immune to evolution of resistance. Relevance to public health: Antibiotics are the most direct and effective approach available against many infectious diseases, but their usefulness is being undermined by the emergence and spread of drug-resistant pathogens. We propose to study a novel strategy for combining antibiotics that may reduce, and perhaps even reverse, the spread of drug resistance while providing an effective treatment paradigm to combat disease. In the longer term, our study will provide a model to be applied to other systems, including other microbial pathogens and cancer.